Research

Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules

Valentina Boeva^1,2 , Julien Clément³ , Mireille Régnier² , Mikhail A Roytberg^4,5 and Vsevolod J Makeev^1,6

¹  Institute of Genetics and Selection of Industrial Microorganisms, GosNIIGenetika, 117545 Moscow, Russia

²  MIGEC, INRIA Rocquencourt, 78153 Le Chesnay, France

³  GREYC, CNRS UMR 6072, Laboratoire d'informatique, 14032 Caen, France

⁴  Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Puschino, Moscow Region, Russia

⁵  Puschino State University, Puschino, Moscow Region, Russia

⁶  Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia

author email corresponding author email

Algorithms for Molecular Biology 2007, 2:13doi:10.1186/1748-7188-2-13

Published:	10 October 2007

Abstract

Background

cis-Regulatory modules (CRMs) of eukaryotic genes often contain multiple binding sites for transcription factors. The phenomenon that binding sites form clusters in CRMs is exploited in many algorithms to locate CRMs in a genome. This gives rise to the problem of calculating the statistical significance of the event that multiple sites, recognized by different factors, would be found simultaneously in a text of a fixed length. The main difficulty comes from overlapping occurrences of motifs. So far, no tools have been developed allowing the computation of p-values for simultaneous occurrences of different motifs which can overlap.

Results

We developed and implemented an algorithm computing the p-value that s different motifs occur respectively k₁, ..., k_s or more times, possibly overlapping, in a random text. Motifs can be represented with a majority of popular motif models, but in all cases, without indels. Zero or first order Markov chains can be adopted as a model for the random text. The computational tool was tested on the set of cis-regulatory modules involved in D. melanogaster early development, for which there exists an annotation of binding sites for transcription factors. Our test allowed us to correctly identify transcription factors cooperatively/competitively binding to DNA.

Method

The algorithm that precisely computes the probability of simultaneous motif occurrences is inspired by the Aho-Corasick automaton and employs a prefix tree together with a transition function. The algorithm runs with the O(n|Σ|(m|| + K|σ|^K) ∏_i k_i) time complexity, where n is the length of the text, |Σ| is the alphabet size, m is the maximal motif length, || is the total number of words in motifs, K is the order of Markov model, and k_i is the number of occurrences of the ith motif.

Conclusion

The primary objective of the program is to assess the likelihood that a given DNA segment is CRM regulated with a known set of regulatory factors. In addition, the program can also be used to select the appropriate threshold for PWM scanning. Another application is assessing similarity of different motifs.

Availability

Project web page, stand-alone version and documentation can be found at http://bioinform.genetika.ru/AhoPro/ webcite